KeXin Zhang


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SEMA: Text Simplification Evaluation through Semantic Alignment
Xuan Zhang | Huizhou Zhao | KeXin Zhang | Yiyang Zhang
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.